Robotic imitation from human motion capture using Gaussian processes

• Published in: 5th IEEE-RAS International Conference on Humanoid Robots, 2005 pp. 129 - 134
• Main Authors: Shon, A.P., Grochow, K., Rao, R.P.N.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 2005
• Subjects: Gaussian processes; Humans; Robots
• ISBN: 0780393201; 9780780393202
• ISSN: 2164-0572
• DOI: 10.1109/ICHR.2005.1573557

Programming by demonstration, also called "imitation learning," offers the possibility of flexible, easily modifiable robotic systems. Full-fledged robotic imitation learning comprises many difficult subtasks. However, we argue that, at its core, imitation learning reduces to a regression problem. We propose a two-step framework in which an imitating agent first performs a regression from a high-dimensional observation space to a low-dimensional latent variable space. In the second step, the agent performs a regression from the latent variable space to a high-dimensional space representing degrees of freedom of its motor system. We demonstrate the validity of the approach by learning to map motion capture data from human actors to a humanoid robot. We also contrast use of several low-dimensional latent variable spaces, each covering a subset of agents' degrees of freedom, with use of a single, higher-dimensional latent variable space. Our findings suggest that compositing several regression models together yields qualitatively better imitation results than using a single, more complex regression model